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Delivering Accurate Evaluation Data in Not-for-Profit Reports

In Brief: Not-for profit organizations are increasingly adopting market-based management approaches as a way to secure a stable position in the contemporary business environment and effectively compete for funding. As a result, not-for-profits are required to fulfill specific contractual obligations and comply with stakeholder reporting policies through the regular delivery of measurable program evaluation data.

The key to delivering graphics that stand for data integrity and respect for the viewer is to choose the best graphical solution to communicate the data simply and clearly.

This study presents not-for-profit managers with a set of factors for consideration in creation of graphical visualizations of quantitative program evaluation data. Two of the most often used types of data graphics are addressed: (1) tables and (2) graphs, both members of a larger family of display methods known as charts. The study examines basic quantitative data visualization concepts necessary to portray program evaluation data accurately and comprehensibly.

Data visualization software offers seemingly endless information presentation options, but it by no means guarantees creation of quality data graphics. One of the most important findings of this research is the need for clear differentiation between graphical data and non-data elements. In order to design successful data graphics, emphasis must be placed on data elements. Non-data elements serve a secondary role and should not distract the viewer from perceiving the actual information.

The key to delivering graphics that stand for data integrity and respect for the viewer is to choose the best graphical solution to communicate the data simply and clearly. The most important task in creating successful graphical data visualizations is to choose appropriate data elements (bars, lines, slices, points, values), and present them using general data organization principles. Graphs can be used for the following data presentation purposes:

  • Bar graphs display information effectively when values to be presented in bars differ significantly; an appropriate bar orientation is chosen (horizontal or vertical); balanced data proximity (distance between bars and width of bars) is maintained; and an appropriate order of data is applied.
  • Line graphs display information clearly when there a sufficient number of values present (three or more data points); a restricted number of data sets is used (five or fewer) in order to avoid the "spaghetti effect"; and lines are distinguished by the means of color or pattern coding.
  • Pie graphs display information successfully when there are a limited number of data sets (five, six, or fewer); slices are arranged by size (bigger to smaller); and a total value of every graph is clearly indicated.

Tables can be used for the following data presentation purposes:

  • To list exact values.
  • To provide precise information and ease of reference.
  • To compare numbers in the same and different categories.
  • To simplify data presentation.

Below is an example of a "before" and "after" table. The "before" table is designed using:

  • Black, point 1 weight gridlines and 1.5 point line for the outer border.
  • 35% gray header shading, and boldfaced header, side and last row text.
  • Centered alignment for text and data.

Results—poor information perception:

  • Visually heavy appearance, gridlines and header row attract main attention. Grid lines are supportive elements and should not dominate the table;
  • Text on dark background is poorly legible.
  • Excessive use of boldface text creates unnecessary emphasis.
  • Centered alignment distracts from perceiving and comparing data.
Figure 1: Before-Poor table formatting example

Figure 1: "Before"-Poor table formatting example

The "after" table is designed using:

  • 5% gray horizontal row shading.
  • 10% gray header shading.
  • Boldfaced text to emphasize categories and total values.

Results—improved information perception:

  • Light shading (horizontal or vertical) can be used to separate values and acts as effective delineator. Shading should be used only when large amounts of data are presented.
  • Boldfaced text emphasizes categories and values, but should be used cautiously as it may distract from perceiving the actual data.
Figure2: After-Improved table formatting example

Figure2: "After"-Improved table formatting example

References

  • Few, S. (2004, August). Common Mistakes in Data Presentation. Intelligent Enterprise. Retrieved May 2, 2005, from Common Mistakes in Data Presentation.
  • O'Neill, E. (2002, September, 4). Non-Profit Hotline: Program evaluation. BusinessNorth. Retrieved April 6, 2005 from Business North.
  • Tufte, E. (1997). Visual explanations: images and quantities, evidence and narrative. Cheshire, CT: Graphics Press.
  • Tufte, E. (2001). Visual display of quantitative information. Cheshire, CT: Graphics Press.
  • W.K. Kellogg evaluation handbook (1998). W.K. Kellogg Foundation.
  • Zelazny, G. (1996). Say it with charts: the executive's guide to visual communication. Burr Ridge, IL: Irwin Professional Publishing.
AIM alumna Ruta Stabina

Research Paper Author: Ruta Stabina–2005 AIM Graduate, Program Coordinator, Continuing Education

Abstract: Graphical presentation of quantitative data greatly improves information perception, absorption, and retention. This literature review study analyzed 16 sources published between 1990 and 2005, addressing the three most frequently used quantitative business data presentation types: tables, graphs, and charts (Tufte, 2001) and graphics design. Results are presented in four tables, providing a set of factors for consideration by not-for-profit organization program managers when creating quantitative graphical data visualizations for use in program evaluation reports.

Download the entire Capstone research project